Polarization

polarization_cces.R

fun <- function(df, var1, var2, weight, yr) {
  
  aaa1 <- df %>% 
    filter({{var1}} <= 7) %>% 
    cces_trad(religpew) %>% 
    group_by(trad2) %>% 
    rename(id := {{var1}}) %>% 
    ct(id, wt = {{weight}}) %>%    
    mutate(year = yr) %>% 
    mutate(group = "Democrats")
  
  
  aaa2 <- df %>% 
    filter({{var2}} <= 7) %>% 
    cces_trad(religpew) %>% 
    group_by(trad2) %>% 
    rename(id := {{var2}}) %>% 
    ct(id, wt = {{weight}}) %>%     
    mutate(year = yr) %>% 
    mutate(group = "Republicans")
  
  bind_rows(aaa1, aaa2)
  
}

yyy1 <- cces12 %>% fun(CC334E, CC334F, weight_vv, yr = 2012)
yyy2 <- cces14 %>% fun(CC334K, CC334L, weight, yr = 2014)
yyy3 <- cces16 %>% fun(CC16_340g, CC16_340h, commonweight, yr = 2016)
yyy4 <- cces17 %>% fun(CC17_350d, CC17_350e, weights_common, yr = 2017)
yyy5 <- cces18 %>% fun(CC18_334D, CC18_334E, commonweight, yr = 2018)
yyy6 <- cces19 %>% fun(CC19_334d, CC19_334e, commonweight, yr = 2019)

graph <- bind_df("yyy") 

gg1 <- graph %>% 
  filter(id == 1 & group == "Democrats") %>% 
  mutate(type = "Very Liberal Democrats") %>% 
  select(trad2, pct, year, type)

gg2 <- graph %>% 
  filter(id == 7 & group == "Republicans") %>% 
  mutate(type = "Very Conservative Republicans") %>% 
  select(trad2, pct, year, type)

graph <- bind_rows(gg1, gg2)

graph %>% 
  ggplot(., aes(x = year, y = pct, color = type, group = type)) +
  geom_point(size=3, color="white") +
  geom_point(size=2, shape=1) +
  geom_point(size=1, shape=19) +
  geom_line() + 
  facet_wrap(~ trad2) +
  theme_rb(legend =  TRUE) + 
  scale_color_manual(values = c("firebrick3", "dodgerblue3")) +
  y_pct() + 
  labs(x = "", y = "", title = "How Would You Describe the Ideology of Democrats and Republicans?", caption = "@ryanburge\nData: CCES 2012-2019") +
  ggsave("E://parties_extreme.png", type = "cairo-png", height = 10)


fun <- function(df, var1, var2, weight, yr) {
  
  aaa1 <- df %>% 
    filter({{var1}} <= 7) %>% 
    filter({{var2}} <= 7) %>% 
    cces_trad(religpew) %>% 
    rename(dem := {{var1}}) %>% 
    rename(rep := {{var2}}) %>% 
    rename(wt := {{weight}}) %>% 
    select(trad2, dem, rep, wt) %>% 
    mutate(year = yr) 
  
}

yyy1 <- cces12 %>% fun(CC334E, CC334F, weight_vv, yr = 2012)
yyy2 <- cces14 %>% fun(CC334K, CC334L, weight, yr = 2014)
yyy3 <- cces16 %>% fun(CC16_340g, CC16_340h, commonweight, yr = 2016)
yyy4 <- cces17 %>% fun(CC17_350d, CC17_350e, weights_common, yr = 2017)
yyy5 <- cces18 %>% fun(CC18_334D, CC18_334E, commonweight, yr = 2018)
yyy6 <- cces19 %>% fun(CC19_334d, CC19_334e, commonweight, yr = 2019)


gg <- bind_df("yyy")

gg <- gg %>% 
  mutate(ex = frcode(rep == 7 & dem == 1 ~ "Both", 
                     rep == 7 ~ "Republicans", 
                     dem == 1 ~ "Democrats", 
                     TRUE ~ "Neither")) %>% 
  as_tibble()

graph <- gg %>% 
  group_by(year, trad2) %>% 
  ct(ex, wt = wt, show_na = FALSE)

graph %>% 
  mutate(pct = round(pct,2)) %>% 
  ggplot(., aes(x = factor(year), y = pct, fill= ex)) +
  geom_col(color = "black") +
  facet_wrap(~ trad2) +
  scale_fill_manual(values = c("darkorchid", "firebrick3", "dodgerblue", "azure3")) +
  theme_rb(legend = TRUE) + 
  y_pct() +
  labs(x = "", y = "", title = "Share Seeing Each Political Party as the Most Extreme", 
       subtitle = "Very Conservative for Republicans/Very Liberal for Democrats",
       caption = "@ryanburge\nData: CCES 2012-2019") +
  geom_text(aes(label = ifelse(pct >.10, paste0(pct*100, '%'), '')), position = position_stack(vjust = 0.5), size = 4, family = "font", color = "black") +
  ggsave("E://polarized_views.png", type = "cairo-png", height = 8, width = 10)



fun19 <- function(df, var1, var2, var3, weight, yr) {
  
  aaa1 <- df %>% 
    filter({{var1}} <= 7) %>% 
    filter({{var2}} <= 7) %>% 
    filter({{var3}} <= 7) %>% 
    cces_trad(religpew) %>% 
    rename(dem := {{var1}}) %>% 
    rename(rep := {{var2}}) %>% 
    rename(self := {{var3}}) %>% 
    rename(wt := {{weight}}) %>% 
    mutate(age = 2019 - birthyr) %>% 
    select(trad2, dem, rep, self, wt, educ, faminc_new, newsint, age, gender, pew_churatd, race) %>% 
    mutate(year = yr) 
  
}

ttt16 <- cces19 %>% fun19(CC19_334d, CC19_334e, CC19_334a, commonweight, yr = 2019)

ttt16 <- ttt16 %>% 
  mutate(ex = frcode(rep == 7 & dem == 1 ~ "Both", 
                     rep == 7 ~ "Republicans", 
                     dem == 1 ~ "Democrats", 
                     TRUE ~ "Neither")) %>% 
  as_tibble() %>% 
  mutate(self = frcode(self == 1 ~ "Very\nLiberal",
                       self == 2 ~ "Liberal",
                       self == 3 ~ "Somewhat\nLiberal",
                       self == 4 ~ "Middle of\nthe Road",
                       self == 5 ~ "Somewhat\nConservative",
                       self == 6 ~ "Conservative", 
                       self == 7 ~ "Very\nConservative"))

gg <- ttt16 %>% 
  group_by(self) %>% 
  ct(ex, wt = wt, show_na = FALSE)


gg %>% 
  mutate(pct = round(pct,2)) %>% 
  ggplot(., aes(x = self, y = pct, fill= ex)) +
  geom_col(color = "black") +
  scale_fill_manual(values = c("darkorchid", "firebrick3", "dodgerblue", "azure3")) +
  theme_rb(legend = TRUE) + 
  y_pct() +
  labs(x = "Respondent's Personal Ideology", y = "", title = "Share Seeing Each Political Party as the Most Extreme", 
       subtitle = "Very Conservative for Republicans/Very Liberal for Democrats",
       caption = "@ryanburge\nData: CCES 2019") +
  geom_text(aes(label = ifelse(pct >.05, paste0(pct*100, '%'), '')), position = position_stack(vjust = 0.5), size = 6, family = "font", color = "black") +
  ggsave("E://polarized_views_2019.png", type = "cairo-png", height = 7, width = 7)


ttt16 <- ttt16 %>% 
  filter(faminc_new <= 20) %>% 
  mutate(ex = frcode(rep == 7 & dem == 1 ~ "Both", 
                     rep == 7 ~ "Republicans", 
                     dem == 1 ~ "Democrats", 
                     TRUE ~ "Neither")) %>% 
  mutate(ex = case_when(ex == "Neither" ~ 0, 
                        TRUE ~ 1)) %>% 
  mutate(male = case_when(gender == 1 ~ 1, gender == 2 ~ 0)) %>% 
  mutate(att = car::recode(pew_churatd, "6=1; 5=2; 4=3; 3=4; 2=5; 1=6; else = NA")) %>% 
  mutate(news = case_when(newsint == 4 ~ 1, 
                          newsint == 3 ~ 2, 
                          newsint == 2 ~ 3, 
                          newsint == 1 ~ 4)) %>% 
  mutate(pid3 = frcode(self == 1 | self == 2 | self == 3 ~ "Liberal", 
                       self == 4 ~ "Moderate",
                       self == 5 | self == 6 | self == 7 ~ "Conservative")) %>% 
  mutate(rac2 = frcode(race == 1 ~ "White", TRUE ~ "Non-White"))

reg1 <- glm(ex ~ pid3*att*rac2 + male + age + faminc_new + educ + news, family = "binomial", data = ttt16 )


int <- interact_plot(reg1, pred= att, modx = pid3, mod2 = rac2, int.width = .76, interval = TRUE, mod2.labels = c("White", "Non-White")) 

int + 
  theme_rb(legend = TRUE) +
  pid3_color() +
  pid3_fill() +
  y_pct() + 
  scale_x_continuous(breaks = c(1,2,3,4,5,6), labels = c("Never", "Seldom", "Yearly", "Monthly", "Weekly", "Weekly+")) +
  labs(x = "Church Attendance", y = "Estimated Likelihood of Seeing\nAt Least One Party as Extreme", 
       title = "Church Attendance Does Little to Reduce Polarized Views among Conservatives", 
       caption = "@ryanburge\nData: CCES 2019") +
  ggsave("E://interact_extreme_views.png", type = "cairo-png")